code: def central(f0, ds, w):
      """Apply central difference method to estimate derivatives."""
      f = lambda o: shift(f0, o)
      eye = np.eye(f0.ndim, dtype=int)
      offsets = [-eye[d] for d in ds]
  
      if not ds:
          return f0
      elif len(ds) == 1:  # First order derivatives.
          i = offsets[0]
          return (f(i) - f(-i)) / (2 * w)
      elif len(ds) == 2:  # Second order derivatives.
          i, j = offsets
          w2 = np.square(w)
          if ds[0] == ds[1]:  # d^2/dxdx
              return (f(i) - 2 * f0 + f(-i)) / w2
          else:  # d^2/dxdy
              return (f(i + j) - f(i - j) - f(j - i) + f(-i - j)) / (
                  4 * w2
              )
      else:
          raise NotImplementedError(ds)
  
file_path: tf-quant-finance/tf_quant_finance/experimental/finite_difference/methods.py
imports: from autograd import elementwise_grad
  import autograd.numpy
  import pandas
  import sobol_seq
label_desc: Use image node_id 5 for calling a global function with example usage: central(f0, ds, w) and returns: f0, unknown, unknown, unknown